code: def _read_extracted_data(
      data_path: Path, split_path: Path
  ) -> Tuple[ArrayDict, ArrayDict]:
      """Reads and processes the extracted graph data and splits."""
      pd = tfds.core.lazy_imports.pandas
  
      # Load columns describing the graph features and structure.
      column_names = [
          "edge_index",
          "num_nodes",
          "num_edges",
          "node_feat",
          "edge_feat",
          "labels",
      ]
      file_names = [
          "edge.csv.gz",
          "num-node-list.csv.gz",
          "num-edge-list.csv.gz",
          "node-feat.csv.gz",
          "edge-feat.csv.gz",
          "graph-label.csv.gz",
      ]
      dtypes = [
          np.int64,
          np.int64,
          np.int64,
          np.float32,
          np.float32,
          np.float32,
      ]
      all_data = {}
      for column_name, file_name, dtype in zip(
          column_names, file_names, dtypes
      ):
          with (data_path / file_name).open("rb") as fp:
              values = pd.read_csv(
                  fp, compression="gzip", header=None
              ).values
              values = values.astype(dtype)
              all_data[column_name] = values
  
      # Load data splits.
      split_indices = {}
      for split_name in ["train", "valid", "test"]:
          with (split_path / ("%s.csv.gz" % split_name)).open(
              "rb"
          ) as fp:
              indices = pd.read_csv(
                  fp, compression="gzip", header=None
              ).values.T[0]
              split_indices[split_name] = indices
  
      return all_data, split_indices
  
file_path: datasets/tensorflow_datasets/datasets/ogbg_molpcba/ogbg_molpcba_dataset_builder.py
imports: from typing import Dict, Text, Tuple
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling a global function with example usage: _read_extracted_data(data_path, split_path) and returns: all_data,  split_indices